Applied Energy 114 (2014) 262–272
Contents lists available at ScienceDirect
Applied Energy
journal homepage: www.elsevier.com/locate/apenergy
A review of electricity product differentiation
C.K. Woo a, P. Sreedharan b, J. Hargreaves b, F. Kahrl b, J. Wang c,e,⇑, I. Horowitz d
a
Department of Economics, Hong Kong Baptist University, Hong Kong
Energy and Environmental Economics, Inc., 101 Montgomery Street, Suite 1600, San Francisco, CA 94104, USA
c
Decision and Information Sciences Division, Argonne National Laboratory, Argonne, IL 60439, USA
d
Warrington College of Business, University of Florida, Gainesville, FL 32611, USA
e
School of Economics and Management, Shanghai University of Electric Power, Ping Yang Road No. 2103, Yangpu District, Shanghai, China
b
h i g h l i g h t s
Electricity has distinct attributes for forming differentiated products.
This paper is a literature review of electricity product differentiation.
It describes real-world examples of product differentiation.
It finds product differentiation improves grid operations and planning.
a r t i c l e
i n f o
Article history:
Received 11 June 2013
Received in revised form 23 August 2013
Accepted 30 September 2013
Keywords:
Product differentiation
Electricity economics
Grid operations and planning
a b s t r a c t
This review is motivated by our recognition that an adequate and reliable electricity supply is a critical
element in economic growth. From a customer’s perspective, electricity has several distinct attributes:
quality, reliability, time of use, consumption (kW h) volume, maximum demand (kW), and environmental
impact. A differentiated product can be formed by packaging its non-price attributes at a commensurate
price. The review weaves the academic literature with examples from the real world to address two substantive questions. First, is product differentiation a meaningful concept for electricity? Second, can product differentiation improve grid operations and planning, thereby lowering the cost of delivering
electricity services? Based on our analysis and comprehensive review of the extant literature, our answer
is ‘‘yes’’ to both questions. We conclude that applying product differentiation to electricity can greatly
induce end-users to more effectively and efficiently satisfy their demands upon the system, and to do
so in an environmentally friendly way.
Ó 2013 Elsevier Ltd. All rights reserved.
Contents
1.
2.
3.
4.
5.
Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Criteria for a useful differentiated product. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Product differentiation under simple metering . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
3.1.
Inclining block rates . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
3.2.
Hopkinson tariff. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
3.3.
Conservation tariff. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
3.4.
Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Enhancements enabled by AMI . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
4.1.
Time-varying pricing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
4.1.1.
Two-part RTP rate option. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
4.1.2.
Customer load response . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
4.2.
Reliability differentiation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
4.3.
Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Future products enabled by two-way communications and competitive bidding . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
5.1.
Competitive bidding for curtailable load . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
5.2.
Generalized demand subscription service . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
⇑ Corresponding author. Tel.: +1 630 252 1474.
E-mail address: [email protected] (J. Wang).
0306-2619/$ - see front matter Ó 2013 Elsevier Ltd. All rights reserved.
http://dx.doi.org/10.1016/j.apenergy.2013.09.070
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5.3.
6.
7.
Competitive bidding for curtailable supply . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
5.3.1.
Customer-side distributed generation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
5.3.2.
Large-scale renewable energy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
5.4.
Renewables firming rate . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
5.5.
Wholesale-market participation for flexible and responsive loads . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Technologies that support and spawn new electricity products . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
6.1.
Smart devices and appliances. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
6.2.
Customer-side storage and electric vehicles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
6.3.
Microgrids and virtual power plants . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
1. Introduction
Product differentiation recognizes that customers have heterogeneous preferences, with varying willingness-to-pay (WTP) for
differentiated products [1,2]. From a customer’s perspective, electricity has several distinct attributes: power quality, level of reliability, time of use (TOU), volume of usage (kW h), maximum
demand (kW), and level of environmental impact. A differentiated
product can be formed by packaging its non-price attributes at a
commensurate price. But because of the unique characteristics of
electricity, customers have historically had only limited options
for purchasing differentiated electricity products.
By canvassing a large body of literature and real-world examples, this review seeks to answer the following questions:
Is product differentiation a meaningful concept for electricity?
Based on our analysis in Sections 3–6, our answer is ‘‘yes’’.
Can product differentiation improve grid operations and planning, thus lowering the cost of delivering electricity services?
Based on the examples described below, our answer is ‘‘yes’’.
This review is motivated by our recognition that an adequate
and reliable electricity supply is critical for an economy’s growth
[3–5], and that electricity outages can be both inconvenient and
cause large financial losses [6–11]. The problem of least-cost grid
operations and planning is difficult, chiefly because of several unique features of electricity. First, unlike other forms of primary energy (e.g., coal, natural gas) or energy carriers (e.g., gasoline,
methanol, hydrogen), electricity cannot be economically stored
and must be supplied in real time to meet randomly fluctuating demand [12]. Second, except for a few end-uses, such as cooking and
heating with natural gas, electricity does not have close substitutes
in real time. As a result, reliably meeting real-time demands requires capacity reserves that can be flexibly dispatched to maintain
an electrical system’s load-resource balance [13–18]. Third, a major facility (e.g., a high-voltage transmission line) can fail unexpect-
Fig. 1. A stylized model of electricity market restructuring.
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edly, with cascading effects that propagate throughout an interconnected network, as dramatically illustrated by the 14 August
2003 outage in the eastern portion of North America [19]. Finally,
capacity additions are lumpy and require long lead times, implying
that construction of new facilities may need to begin even when an
electricity system has a capacity surplus, and well before the realization of an anticipated growth in demand [20,21].
The electricity industry has traditionally been dominated by
vertically-integrated utilities. In making least-cost operations and
planning decisions, each utility has full control of its locational resources and their additions, and a wealth of information as to its
loads by individual location, as well as projections for future locational demands on the system [13–18,22,23]. In the last two decades, however, the industry has witnessed three transformative
events.
The first event is market restructuring to introduce wholesalemarket competition in Australia, New Zealand, parts of North and
South America, and Europe [24–33]. The second event is government-supported large-scale development of renewable energy
(e.g., hydro, wind, solar, geothermal, biomass, and landfill gas) in
North America, Europe, and Asia in response to concerns over environmental pollution, climate change, and energy security [34–47].
The third event is the technological advances in advanced metering
infrastructures (AMI) and smart grids [48–56].
With regard to the first event, market restructuring has substantially complicated the least-cost operations-and-planning
problem because it transforms an industry once dominated by
integrated utilities into one that relies on competition to deliver
generation and retail services. Fig. 1 portrays a stylized model of
market restructuring wherein customers can buy electricity directly from wholesale generation markets (i.e., the power pool
and the bilateral market), or through load-serving entities (LSEs),
such as local distribution companies (LDCs) and retailers that procure power from wholesale generation markets [24–33]. The LSEs
may be obligated to procure renewable energy to comply with
mandatory targets under a renewable portfolio standard (RPS)
set by the government [35,37–39,42–47]. Since spot electricity
prices are highly volatile with occasional spikes [57–68], LSEs
and customers may seek to manage electricity spot-price risk using
such hedge instruments as, tolling agreements, forward contracts,
and capacity options that are traded in financial markets [69–74].
Though not shown in Fig. 1, an independent system operator
(ISO) leases transmission facilities, performs generator dispatch,
manages congestion, sets the pool’s market-clearing prices, and
administers an open-access transmission tariff that offers fair and
comparable access to all market participants. The ISO does not
own any generation or transmission resources, but it nonetheless
has responsibility for real-time operations and long-term transmission planning. As wholesale energy market prices are likely to
be insufficient to induce investment in conventional generation
[75,76], the ISO may operate a capacity market, in addition to the
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power pool, to procure the capacity that will ensure resource adequacy [77–79]. When compared to an integrated utility, the ISO
has very little control over non-transmission resource output and
investment, and hence faces greater uncertainty regarding generator behavior and the timing and location of new resource additions
[80–87].
With regard to the second event, large-scale development of
renewable generation introduces a substantial risk previously unseen by an ISO. Consider wind generation, which has large and random output fluctuations, highly unpredictable availability, and
limited dispatchability in reducing (but not increasing) output.
Since wind generation has zero fuel cost, the ISO economically dispatches wind generation to displace marginal generation with high
fuel cost, unless curtailed to resolve grid congestion and instability
[88–89]. Hence, wind-generation expansion reduces wholesale
spot electricity prices and diminishes the incentive for naturalgas-fired generation investments [90–95], even when such investments are urgently needed to integrate new wind-generation
capacity into the grid [96–98].
With regard to the third event, AMI and smart grid technologies
enable electricity product differentiation that was infeasible
20 years ago [5,48–53]. As will be shown below, such technologies
help implement locational real-time pricing with frequently updated hourly prices, reliability differentiation with two-way communications, and competitive bidding that determines marketclearing prices and allocation of electricity resources.
In light of these three events, this review contributes to the
electricity economics literature by using real-world examples to
demonstrate that product differentiation can improve electricity
grid operations and planning. Aimed to enrich the dialogue on demand response [99–105], it considers traditional products under
simple metering [106,107], recent enhancements enabled by AMI
[108–113], and future products that take advantage of two-way
communications and competitive bidding in a smart grid with
AMI. Hence, the paper’s information will be of interest to policymakers and analysts in many nations, including China whose electricity industry is now undergoing regulatory and market reforms,
seeing large-scale renewable development, and the implementation of AMI and smart-grid technologies [114–132].1
2. Criteria for a useful differentiated product
We propose a set of criteria to determine if a differentiated
product is useful for grid operations and planning. For expositional
ease and concreteness, we assume that the product is provided by
a regulated LDC, subject to cost-of-service regulation [107]. This
assumption reflects the current market environment in most parts
of North America, even though active retail competition exists in
some states (e.g., New York and Texas).
Our proposed criteria are as follows:
Financial viability. The differentiated product should be financially viable and yield revenue that is sufficient to cover the
LDC’s cost of provision. This ensures that the product’s purchase
by some customers does not lead to losses to the LDC, which in
turn causes bill increases to other customers [110].
Customer acceptance. The differentiated product should be
acceptable to customers, because a product that few customers
want cannot play a meaningful role in the grid operations-andplanning decision process. A case in point is real-time pricing
(RTP) [22,133], which bills electricity consumption at time1
As pointed out by an insightful referee, a comprehensive discussion of the
applicability and usefulness of electricity differentiation in China is well beyond the
scope of this paper. That said, this is an important topic that should be explored in a
separate paper.
varying prices and is unattractive to small customers because
of the product’s complexity and price volatility; this is notwithstanding that industrial RTP has been implemented in such
states as North Carolina, New York and Texas [134–136].
Customer engagement. The product should exploit the operational flexibility offered by two-way communications and smart
devices. Notable examples are remotely- activated programmable thermostats for space cooling, and instantaneous fuel
switching between natural gas and electricity for space and
water heating [51,110].
Reliability management. The product should help balance locational demands and supplies, because imbalances jeopardize
reliability, potentially triggering voltage collapse and cascading
outages, with ensuing significant economic losses [6–12,19]. It
should also help a system to meet the reliability standards of
a governing body, such as the North American Electricity Reliability Corporation (NERC), which sets the one-day-in-10-years
loss-of-load-expectation (LOLE) standard for planning reserves
and the seven-percent-of-peak-demand standard for operating
reserves [137].
Asset utilization. The product should improve utilization of
existing assets and reduce demand spikes, thus diminishing
the need for new investments. A good example is reliability differentiation that enables the efficient allocation of limited
capacity during severe power shortages [109–113,138–139].
Renewable-energy development. The product should help
develop and integrate the intermittent renewable resources,
such as solar and wind, that are critical for achieving a clean
and sustainable electricity supply [5,42,51–53,112,113,140]. A
case in point is a low super-off-peak charging rate for electrical
vehicles (EV) to avoid curtailment of excessive wind energy
during early morning hours (e.g., 02:00–05:00) when system
loads are low [111,141–143].
Greenhouse gas (GHG) emissions reductions. The differentiated
product should help reduce GHG emissions by (a) vehicles that
use gasoline and diesel, and (b) generation plants that burn fossil fuels, especially coal [144]. While super-off-peak EV charging
with wind energy can help reduce vehicle emissions, reducing
output from coal-fired plants requires conservation actions
and energy efficiency investment by customers, which can be
encouraged by a product that discourages consumption
[145,146].
3. Product differentiation under simple metering
Product differentiation is limited by the metering technology in
place. A simple meter that records a customer’s monthly kW h consumption can only support products whose prices vary by kW h
volume. A more sophisticated meter that records a customer’s
monthly kW h and maximum kW can support products with
kW h and kW differentiation. This section first discusses these
products based on nonlinear pricing [106,109]. It then describes
a recent product that aims to strengthen the price signal for conservation in an environment of rising cost for new supplies.
3.1. Inclining block rates
Used by an LDC for its default service for residential customers
that have not made an explicit selection for an optional service
(e.g., TOU pricing), inclining block rates have been a part of U.S. energy policy since the 1970s. Summer inclining block rates are well
established on the West Coast and in Southwestern states and winter inclining block rates are widely used in the West, Southeast,
and Great Lakes regions [146].
The popularity of inclining block rates in the U.S. is due in large
part to Section 111 of the 1978 Public Utility Regulatory Policy Act
C.K. Woo et al. / Applied Energy 114 (2014) 262–272
(PURPA), which discourages electric utilities from using declining
block rates with volumetric discounts, because such rates likely
encourage residential electricity consumption. Section 114 of the
same act permits lower lifeline rates for ‘‘essential needs (as defined by the State regulatory authority. . .).’’ The implication of
these two sections is that LDCs should employ an inclining block
tariff that has a relatively low ‘‘lifeline’’ rate for residential customers when meeting their ‘‘essential needs,’’ and a higher rate for
usage in excess of those needs.
To see the conservation incentive of inclining block rates, consider a two-tier tariff with per kW h rate P1 that applies to a customer’s monthly consumption up to the K-kW h threshold and P2
that applies to consumption above K. The customer’s monthly bill
for Q kW h of consumption is:
B ¼ P1 minðQ ; KÞ þ P 2 maxðQ K; 0Þ:
For a customer with a rate of consumption Q that is above the
tier-1 threshold K, the average rate is P = [P1K + P2 (Q K)]/Q,
which is increasing at a decreasing rate with Q. As Q increases, P
converges to P2. Hence, the tariff discourages consumption by large
users, by charging a higher price P2 for a higher rate of usage Q.
Residential conservation can be seen as the outcome of a customer’s two-stage decision process [147–149]:
Stage 1: Given its income, preferences, and demographics, the
customer selects a portfolio of energy-using durables based on
the trade-offs between the portfolio’s capital cost and the
expected operating cost, so as to achieve its preferred levels of
end-uses (e.g., space conditioning, water heating, lighting, cooking, clothes washing and drying, and dishwashing).
Stage 2: The customer determines utilization of its chosen portfolio, based on the electricity rates and its income, preferences,
and demographics.
When compared to a flat rate, inclining block rates have two
conservation effects. In Stage 1, the rates magnify the operating
cost of an energy-inefficient portfolio for usage extending beyond
the lower-priced first tier. Ceteris paribus, the operating-cost increase induces customers to select a more energy-efficient portfolio. In Stage 2, the inclining block rates discourage utilization of the
chosen portfolio, because they result in a per kW h payment that
rises with consumption. Taken together, these two effects reinforce
residential conservation, as confirmed by the conservative range of
negative price elasticity estimates of 0.1 to 0.3 for residential
electricity consumption [147–154].
3.2. Hopkinson tariff
A simple Hopkinson tariff, which is typically applied to an LDC’s
default service for large non-residential customers such as industrial plants and commercial buildings, has a linear per kW h rate
that applies to a customer’s monthly kW h consumption in a billing
month, and a linear per kW charge that applies to that customer’s
monthly maximum kW [155,156]. While the tariff discourages
the customer from spiking its kW demand, it tends to encourage
the customer to increase its kW h consumption, so as to spread
the kW charge over more kW h [157]. Thus, the tariff helps improve a system’s load factor and hence capacity utilization.
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(large) users with consumption below (above) the first-tier threshold will see a rate decrease (increase), thus increasing (decreasing)
their consumption. Overall conservation occurs only when the
small users’ consumption increase is more than offset by the large
users’ consumption decrease.
To ensure that every customer sees a conservation price signal,
consider the two-part conservation tariff adopted in 2011 by BC
Hydro for general service customers with monthly demands in excess of 150 kW [145,158]. Under this tariff, a customer’s bill has
two parts. The first part is the customer’s historical kW h consumption and kW demand billed at the old rate design.2 The second part
is the customer’s consumption change (relative to the customer’s
historical levels) priced at BC Hydro’s relatively high long-run marginal cost (LRMC), as measured by the incremental cost paid by BC
Hydro for new resources.
The two-part billing implies that a customer’s incremental (decremental) consumption is always charged (credited) at the LRMC,
which tends to be high for an LDC that is required to meet a high
RPS target (e.g., 33% of retail sales by 2020 in California) [159]. Hence,
all customers will see an LRMC-based price signal for conservation,
which is an important attribute of an energy-efficient society.
3.4. Evaluation
Inclining block rates and a conservation tariff are effective in
reducing an LDC’s energy requirements and helping achieve a
GHG reduction, when the requirement is largely met by conventional generation that burns fossil fuels. They are ineffective in customer engagement, however, because a customer may be unaware
of the rates that it is paying on a daily basis [150].
Consider a residential customer with a meter installed outside
the home. At the start of a billing period, the customer faces the
tier-1 rate. The customer then pays the tier-2 rate after its cumulative consumption within the billing period exceeds the threshold.
Unless the customer knows its daily consumption and what the
LDC charges for that consumption, it does not have the clear daily
price signals that would impel its conservation decisions. Thus, an
immediate improvement is information feedback through an
in-home display, which has been found to induce reductions in
consumption of as much as 14% [160].
Inclining block rates and a conservation tariff are also ineffective for reliability management, asset utilization, and the development of renewable energy. This is because they do not provide
incentives for customers to reduce their kW demands during
capacity shortages, or to increase their demands when there is
abundant wind energy. Hence, in the next section we consider
enhancements made possible by AMI.
4. Enhancements enabled by AMI
Marginal-cost pricing of electricity is economically efficient
[23,106,108]. Since an electrical system’s marginal costs vary by
time and location [22,161–164], RTP by location has been implemented as locational marginal-cost pricing (LMP) [133,165] in
New York, New England, PJM, Texas, and California. But LMP is seldom used by LDCs, chiefly due to concerns of customer acceptance
and understanding, bill impacts, and tariff complexities.
4.1. Time-varying pricing
3.3. Conservation tariff
Replacing an existing flat rate for kW h consumption with
inclining block rates does not necessarily lead to overall conservation at the system level. This is because cost-of-service regulation
requires that the revenue collected under the inclining block rates
be the same as the revenue under the flat rate. As a result, small
Real-time pricing for an LDC’s large customers can occur via a
mandatory tariff or as a rate option [134–136]. Replacing an
2
Designed to limit a kW demand spike and encourage kW h consumption, the old
design is a Hopkinson tariff with inclining block rates for kW demand and declining
block rates for kW h consumption.
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existing tariff (which can be TOU for large users or non-TOU for
small users) with mandatory RTP, is often unacceptable to customers, however, because of the potentially large and adverse bill impacts. One possibility is to modify the existing tariff so that high
prices are transmitted to a customer only on system peak days,
resulting in critical-peak pricing (CPP) [166]. There are, however,
other alternatives that customers may find more acceptable, as
described in the remainder of this section.
4.1.1. Two-part RTP rate option
A rate option allows a customer to choose between the default
service tariff and an alternative tariff, which is similar to a prospective home owner being able to choose between a fixed-rate and a
variable-rate loan. Since the customer can make the choice that
best suits its needs, from the customer’s perspective an optional
RTP is generally preferable to mandatory RTP.
Designing an RTP rate option can be challenging because of adverse self-selection by customers [110]. A case in point is a customer served under a simple Hopkinson tariff. If the customer
has relatively low usage during the daytime hours, it may achieve
bill savings by taking the RTP option, without providing any benefit
to the electricity system. As the LDC sees large losses from offering
the option, it may raise the rates for the other customers, who may
in turn oppose the option.
A two-part RTP option such as was adopted by B.C. Hydro in
1996, circumvents the adverse self-selection problem [167]. The
first part is the customer’s historical consumption profile billed
at the otherwise applicable Hopkinson tariff. The second part is
the customer’s hourly consumption deviation (which can be positive or negative) billed at the RTP hourly rates. The option is expected to break even if each hourly rate is set at the wholesale
market price for that hour, and to be profitable if each hourly rate
is set at the equally-weighted average of the Hopkinson tariff’s energy charge and the wholesale market price for that hour.3 Though
remarkably simple, this design ensures that no customer can have
bill savings without reducing (increasing) consumption during the
high-price (low-price) hours.
A variant of two-part RTP is the two-part TOU rate option
[145,168]. Instead of hourly pricing at the wholesale market price,
the TOU option’s on-peak and off-peak rates can be set at the
wholesale market’s on-peak and off-peak forward prices, which
are 6–10% higher than the expected spot prices [169].
entiation is DSS, which stipulates that before receiving service, a
customer must subscribe to a firm service level (FSL). The customer
pays a firm demand charge posted by the LDC for each kW in the
FSL and per kW h usage rates by TOU for the kW h consumed.4
The usage rates may be nonlinear, as in an inclining block tariff. To
be sure, not all customers would make an explicit FSL selection.
The default FSL for these customers may be based on their historical
peak kW.
The DSS encompasses the commonly used non-firm rate options used in North America:
Interruptible service. The customer set its FSL = 0 so that the
LDC can interrupt its entire load during a system emergency.
End-use-specific interruptible service (e.g., air conditioning (AC)
load shedding). The customer’s FSL includes the non-AC loads
but not the AC loads. Thus, the LDC can only interrupt the customer’s AC load during a system emergency.
Curtailable service. The customer sets its FSL below its peak kW
demand so that the difference between the peak kW demand
and the FSL is its curtailable load. Upon the LDC’s request, the
customer reduces its total load below the self-chosen FSL during
a system emergency.
End-use-specific curtailable service (e.g., a remotely-activated
smart thermostat for air conditioning). A customer’s FSL
includes the non-AC loads and a portion of the AC loads.
With regard to grid operations, DSS efficiently allocates the limited capacity during a shortage, because customers with relatively
high outage costs tend to subscribe higher FSLs than do those with
relatively low outage costs [138,188–190]. As the capacity allocation
is based on each customer’s FSL selection, more capacity will go to
customers that place a greater value on reliable electricity service.
With regard to grid planning, the DSS induces customers to reveal their demands for firm capacity, greatly reducing the system’s
reserve requirements. Consider, for example, California’s planning
reserve requirement of 15–17% of the system-peak forecast under
extreme summer weather [192]. This requirement can be reduced
by a system-wide implementation of DSS, because the system’s
firm-load obligation is readily computed as the sum of all
customer-specific FSL subscriptions and these are known with certainty, prior to each customer’s actual usage.
4.3. Evaluation
4.1.2. Customer load response
Time-varying pricing aids grid operations and planning by
reducing system peak consumption and promoting off-peak consumption [99–104]. The size of this benefit depends on customer
load response to time-varying pricing. Empirical evidence suggests
that customer load response sans enabling technologies such as
programmable thermostats, is moderate, with low own-price elasticity estimates of between 0.0 and 0.1 [100,134–136,170–182].
With enabling technologies in place, a customer can cut its kW
demand by as much as 50% [183–187]. This underscores the
importance of smart devices in a customer’s ability to respond to
time-varying pricing.
Products based on time-varying pricing and reliability differentiation exploit the billing flexibility offered by AMI and make limited use of two-way communications and smart devices. They help
balance locational demands and supplies and reduce the system’s
reserve requirements. Since they reduce demand spikes, they improve utilization of existing assets and diminish the need for
new investments. They help integrate intermittent renewable resources because (1) they convey low price signals when renewable
energy is abundant, and (2) they resolve shortages when renewable energy output suddenly drops [113]. Finally, they can help reduce GHG emissions when implemented as rate options that
complement tariffs with strong conservation signals [111].
4.2. Reliability differentiation
Reliability differentiation can be implemented as a demand
subscription service (DSS), a curtailable service, or an interruptible
service [113,138,156]. A useful representation of reliability differ3
This is because the hourly rate during a high-market-price hour leads to reduced
consumption and cost savings that can more than offset the revenue loss. Similarly,
the hourly rate during a low-market-price hour leads to increased consumption and
revenue gains that can more than offset the cost increase.
5. Future products enabled by two-way communications and
competitive bidding
This section considers products beyond the enhancements
discussed in Section 4. All these products entail two-way
4
Alternatively, the customer may receive a bill credit for the curtailable loads and
usage associated with those loads [191].
C.K. Woo et al. / Applied Energy 114 (2014) 262–272
communications and competitive bidding, foretelling what customers may see in the future. While they can further improve grid
operations and planning, they have additional implementation
costs. Thus, their net benefits should be considered prior to their
adoption.
5.1. Competitive bidding for curtailable load
An LDC may use competitive demand bidding to engage customers on a daily basis to provide a grid’s operating reserve. A
competitive Vickrey auction entails a customer’s submitting a multi-dimensional bid in response to an LDC’s daily request for a curtailable-load target (e.g., 50 MW). Each bid is required to specify the
amount of load that can be curtailed, along with the per kW upfront payment and the per kW h usage price for each of the curtailable kW [193].5 The LDC selects the submitted bids with the lowest
per kW payments to meet the curtailable-load target. All winning
bidders are paid the lowest upfront payment of the last rejected
bid. When needed, the LDC dispatches the selected bids, starting
with the one with the lowest per kW h usage rate. All dispatched
bids receive the market-clearing price. The resulting bid selection
and dispatch are the least-cost solutions for achieving the curtailable-load target in a competitive auction setting.
5.2. Generalized demand subscription service
Generalized demand subscription service (GDSS) [111] is a variant of the simple DSS of Section 4. GDSS requires that each customer make an FSL subscription (e.g., 100 kW) before receiving
electricity service. The customer may then alter its position (e.g.,
from the first to the last one to be curtailed) in the curtailment
queue, if the LDC can accommodate the request and the customer
is willing to pay a charge for the cost of queue adjustment. Moreover, the customer can segment its FSL subscription such that each
has a self-stated curtailment price trigger (e.g., the first 10 kW at
$2/kW h, the next 50 kW at $5/kW h, and so forth). In effect, the
customer informs the LDC of its willingness-to-accept (WTA) additional curtailment, beyond the non-firm load declared under its FSL
selection. The customer can update the FSL delineation on a daily
basis, so as to meet its changing preferences (e.g., raising the
self-stated price triggers by an industrial user to meet a production
deadline).
With regard to grid operations, the LDC uses the GDSS to efficiently allocate limited capacity during shortages, based on each
customer’s curtailable load and FSL delineation. With regard to grid
planning, the GDSS induces customers to reveal their preferences
for firm capacity. In particular, if customers indicate low WTAs
for reliability deterioration, even infrequent system expansions
should not occur. When the LDC frequently makes high WTA-based
payments for load curtailments, system expansion should occur to
resolve any shortage.
5.3. Competitive bidding for curtailable supply
5.3.1. Customer-side distributed generation
New products that leverage smart-grid infrastructures could
facilitate the integration of distributed generation (DG). Customer-side DG is currently treated as must-take by LDCs. High
penetration of DG can create issues on the distribution network
such as voltage regulation. Smart- grid technologies, however,
could allow owners of these systems to curtail their supplies or
cede control of curtailment to their LDC, subject to the terms of a
5
The amount of curtailed energy should reflect the customer’s curtailable kW’s
load factor. For example, if the load factor is 0.5, the curtailed energy per kW per hour
is only 0.5 kW h, not 1 kW h.
267
contractual agreement, as is common with large-scale renewable
generation. Since customer-side DG is profit motivated, any
curtailment product that makes both the LDC and DG owners economically better off has the potential for significant uptake. Benefits to the LDC could include reduced investment in transmission
infrastructure, and lower flexibility reserve requirements. Just as
smart-grid technologies allow differentiation of load-based products, competitive bidding and real-time pricing frameworks can
be made available to suppliers.
Competitive bidding, as described in Section 5.1, could be used
to establish an efficient ordering of DG owners willing to be curtailed, producing the least-cost solution to curtailment of supply,
and creating a new market in curtailable energy. Efficient supply
curtailment can also be achieved through RTP: when the price is
lower than marginal cost less production incentive payments, a
profit-maximizing generator will shut off. RTP, however, may dampen investment in DG technologies. The losses from curtailment
under RTP incurred by the DG owners expose them to open-ended
risk on their investment.
5.3.2. Large-scale renewable energy
Large-scale renewable generators already tailor their powerpurchase agreements (PPAs) to the levels of curtailment risk they
are willing to accept. Greater risk commands higher power prices.
A more efficient solution to curtailment of these units could be
achieved, however, through the competitive bidding market for
curtailable supply, as outlined above. There would be no need to
separate the markets for renewable generators.
5.4. Renewables firming rate
A renewables firming rate strives to maintain reliability with
increasing renewable generation. It encourages customers to exercise their controllable resources to neutralize the impacts from distributed renewable generation.
The rate operates as follows. The LDC offers a bill discount to a
customer, in exchange for its commitment to stay within a bandwidth (e.g., ±5%) of its prescheduled net load. When the customer’s
load deviations exceed the bandwidth, the LDC imposes a per kW h
penalty on the deviations.
After joining this rate, the customer would submit a schedule
based on its anticipated net electrical needs, taking into account
onsite generation from renewables. In real time, the customer
would seek to maximize the returns of this rate, using its monitoring systems and controllable loads to manage its net load requirements, based on actual electrical needs and renewable generation.
5.5. Wholesale-market participation for flexible and responsive loads
Thus far, we have assumed product differentiation primarily
implemented by an LDC. This assumption should be relaxed to reflect the continuous evolution of the electricity industry. Here we
describe a case in point: flexible load response as a resource bidding into current and future ancillary services markets for enabling
the integration of renewable energy. Using demand-side resources
to support renewables integration has been discussed in the literature along with the advantages of using load-based resources for
meeting this need, over traditional capacity resources [194–196].
Grid operators currently manage variability in the system at the
sub-hourly level [197,198] through short-term energy-imbalance
markets and ancillary services markets (spinning, non-spinning,
frequency regulation services). With a few exceptions, these services are provided by generators. Loads, however, are increasingly
being considered for renewables integration services, because they
may be able to do so more cost-effectively and with faster response
times. Utilizing load-based resources in a significant way will,
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however, require a change from the event-based demand-response
paradigm that currently exists, to one that emphasizes the quality
of the resource. Products will need to be structured and priced to
value quantity and flexibility [194].
A multitude of load types could provide grid-integration services through wholesale markets, such as university campus
microgrids, motor loads of manufacturing plants and wastewater
treatment facilities, electric vehicle fleets and large thermal storage
facilities. Electric vehicles, aluminum smelting, and oil extraction
from tar sands and shale deposits are described as especially
flexible loads [195]. AMI and the IT infrastructure that is part
and parcel of a smart grid could motivate direct load-control
strategies in which either a utility or third-party aggregator could
control smaller commercial and residential loads [196].
6. Technologies that support and spawn new electricity
products
can be programmed to respond to price signals and can also be
used in direct load-control products.
Accompanying smart appliances are home energy management
(HEM) systems. Since most price-signal products apply to total
load, it is necessary to optimize the load for an entire home, taking
into account the diversity, interruptibility and controllability of
individual household loads [201,202]. Advanced algorithms may
improve the performance of an HEM [203,204]. Coordinated
control extends beyond the household to the utility level, thus preventing new peak-load problems that may occur when multiple
homes autonomously respond to a single price signal [205].
There are tradeoffs between the implementation cost of these
control strategies and their benefits. For instance, power monitoring of individual appliances may be prohibitively expensive, and
estimation methods may be used for implementing low-cost
load-management strategies [206,207].
6.2. Customer-side storage and electric vehicles
New products and tariffs can drive technological innovation,
and technology innovation can spawn new products. Fig. 2 shows
that technology impacts load, which in turn determines the value
of a differentiated product (e.g., RTP). Load impacts may provide
a regulation service on a second-by-second basis or yield permanent load shifting sustained over hours. The product induces a load
impact via a price signal (e.g., dynamic pricing) or direct load control (e.g., an externally controllable thermostat).
The load impact can come from an individual device or systems
of devices, which may be energy consuming or generating. The
scale of the system may range from a university microgrid controller—which controls multiple buildings, onsite generation, thermal
storage, and other central mechanical systems—to individual
residential appliances remotely controlled by two-way
communications.
To connect the economics and engineering aspects of new products, the remainder of this section discusses three examples of
technology: smart products, thermal energy storage and electric
vehicles, and microgrids and DG.
6.1. Smart devices and appliances
Smart devices, along with AMI and two-way communications,
allow residential customers to take advantage of the products described in Section 5. While commercial and industrial customers
have had interval meters and digital energy management systems
for decades, residential customers have historically not had the
metering, controllability, or communications systems. Going forward, however, residential customers can utilize the communications and networking technologies for energy management and
home automation described in [199,200]. A case in point is smart
appliances already available from some manufacturers, which
Storage systems bring flexibility to consumer loads and facilitate the use of the products described in Section 5, including
demand subscription and renewables integration services. Customer-side storage is available in batteries (stationary and electric
vehicles), thermal systems (chilled-water tanks, water heaters,
refrigerated warehouses), and in industrial process control (altering industrial motor loads).
Thermal energy storage systems are particularly well suited for
products that encourage load shifting [208,209]. For example, TOU
and RTP pricing encourage consumers to store energy during lowprice hours for later use during high-price hours. Similarly, reliability differentiation via GDSS may target vehicle charging, which can
be curtailed with little impact to the customer.
Coordinated charging of multiple devices through an aggregator
or supervisory control system can create scaled effects and ancillary services for integrating renewables. Since wind generation is
often night peaking, these devices in large numbers can reduce a
grid’s fast-ramping reserve requirements [210]. For example, electric vehicles can be used to smooth wind generation and to supply
flexible loads [211–213].
6.3. Microgrids and virtual power plants
Microgrids allow customers to manage their net loads by controlling a mix of generation, storage, and energy-consuming devices, thereby operating as virtual power plants. A case in point
is the microgrid of the University of California, San Diego (UCSD),
which has combined heat and power systems, thermal energy storage, electric and steam chillers, and visibility and controllability of
multiple buildings. UCSD has around 2 MW of solar PV, advanced
solar forecasting, a fuel cell, and electric vehicle charging stations
to contribute towards flexible operations.
UCSD currently manages its electrical loads during system peak
hours by discharging thermal storage tanks during the day,
limiting electric chiller operations and operating CHP systems.
With improved predictive and operational tools, these systems
can be further optimized to minimize costs under RTP and GDSS.
7. Conclusion
Fig. 2. Relationship between technological systems and electricity products.
One can, and many have, debated the chicken-and-egg relationship between economic growth and electricity consumption [3,4].
What is indisputable, however, is that the two are inextricably
intertwined. It is therefore equally incumbent upon policy makers
in the public sector and senior management in the electricity
industry to embrace rapidly changing technologies, particularly
C.K. Woo et al. / Applied Energy 114 (2014) 262–272
smart-grid technologies and advanced metering infrastructures, in
a world that is increasingly concerned with environmental issues
and hence the development of non-polluting renewable-energy resources. One such direction, the one that has been the focus of this
survey, is the provision of differentiated products to electricity customers – which means virtually every person and business in all
but the most primitive societies.
Suppliers have a plethora of options by which they can differentiate their products, and those options are expanding as technological advancements occur unabated. With reliability of supply a
prime consideration, the options run a gamut from allowing customers to choose between various rate structures and offering a
demand subscription service that allows customers to pay for their
places on the cut-off ladder in the event that a supplier is overwhelmed by demands on the system, to smart grids armed with remotely-activated demand-response control devices, through
which electricity usage becomes instantaneously sensitive to price
changes. Energy conservation and encouraging the development
and use of renewables are also accorded pride of place in the policy-making hierarchy of objectives. There is, in effect, an emerging
partnership between energy consumers and energy providers
wherein the industry is developing products designed to induce
end-users to more effectively and efficiently satisfy their demands
upon the system, and to do so in an environmentally friendly way.
With great appreciation, we applaud these efforts.
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